{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "b75bf94a",
   "metadata": {},
   "source": [
    "## EfficientNet V1\n",
    "\n",
    "<img src=\"resources/efficientnetv1_perf.png\" alt=\"drawing\" width=\"60%\"/>\n",
    "\n",
    "无论是提高网络深度,增加网络宽度和提高输入图像的分辨率都可以提升网络的容量从而提升网络的精度. EfficientNet V1提出了可以联合调整深度,宽度和输入图像的分辨率,使得在固定的计算开销和访存开销下求得最优网络配置. 它采用Grid Search寻得最优的参数配置\n",
    "\n",
    "<img src=\"resources/efficientnet_v1_formula.png\" alt=\"drawing\" width=\"40%\"/>\n",
    "\n",
    "具体而言,EfficientNet V1:\n",
    "\n",
    "* MBConv 模块: 源自 MobileNet V2 的倒置残差块，并在 MobileNet V3 中进一步优化，EfficientNet V1 将其作为主干网络的基础构建单元 \n",
    "* SE 注意力机制: 直接引入了 SENet 的 SE 模块，通过通道注意力增强特征表达能力 \n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "72e09dca",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Use device:  cuda\n"
     ]
    }
   ],
   "source": [
    "# 自动重新加载外部module，使得修改代码之后无需重新import\n",
    "# see http://stackoverflow.com/questions/1907993/autoreload-of-modules-in-ipython\n",
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "\n",
    "from hdd.device.utils import get_device\n",
    "\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.optim as optim\n",
    "from torchvision import datasets, transforms\n",
    "\n",
    "# 设置训练数据的路径\n",
    "DATA_ROOT = \"~/workspace/hands-dirty-on-dl/dataset\"\n",
    "# 设置TensorBoard的路径\n",
    "TENSORBOARD_ROOT = \"~/workspace/hands-dirty-on-dl/dataset\"\n",
    "# 设置预训练模型参数路径\n",
    "TORCH_HUB_PATH = \"~/workspace/hands-dirty-on-dl/pretrained_models\"\n",
    "torch.hub.set_dir(TORCH_HUB_PATH)\n",
    "# 挑选最合适的训练设备\n",
    "DEVICE = get_device([\"cuda\", \"cpu\"])\n",
    "print(\"Use device: \", DEVICE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "eb715028",
   "metadata": {},
   "outputs": [],
   "source": [
    "from hdd.dataset.imagenette_in_memory import ImagenetteInMemory\n",
    "\n",
    "train_dataset = ImagenetteInMemory(\n",
    "    root=DATA_ROOT,\n",
    "    split=\"train\",\n",
    "    size=\"full\",\n",
    "    download=True,\n",
    "    transform=None,\n",
    ")\n",
    "val_dataset = ImagenetteInMemory(\n",
    "    root=DATA_ROOT,\n",
    "    split=\"val\",\n",
    "    size=\"full\",\n",
    "    download=True,\n",
    "    transform=None,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "072062de",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-----------------------efficientnet_b0------------------------\n",
      "#Parameter: 4020358\n",
      "Epoch: 1/100 Train Loss: 2.8185 Accuracy: 0.1724 Time: 10.02875  | Val Loss: 2.0845 Accuracy: 0.3429\n",
      "Epoch: 2/100 Train Loss: 2.1487 Accuracy: 0.2980 Time: 9.71126  | Val Loss: 2.0542 Accuracy: 0.4280\n",
      "Epoch: 3/100 Train Loss: 1.9668 Accuracy: 0.3702 Time: 9.68002  | Val Loss: 1.7078 Accuracy: 0.4606\n",
      "Epoch: 4/100 Train Loss: 1.9192 Accuracy: 0.3958 Time: 9.66451  | Val Loss: 1.6149 Accuracy: 0.5203\n",
      "Epoch: 5/100 Train Loss: 1.7571 Accuracy: 0.4643 Time: 9.68285  | Val Loss: 1.4182 Accuracy: 0.6168\n",
      "Epoch: 6/100 Train Loss: 1.6698 Accuracy: 0.5080 Time: 9.73478  | Val Loss: 1.5183 Accuracy: 0.5679\n",
      "Epoch: 7/100 Train Loss: 1.5784 Accuracy: 0.5434 Time: 9.62166  | Val Loss: 1.2792 Accuracy: 0.6757\n",
      "Epoch: 8/100 Train Loss: 1.5158 Accuracy: 0.5748 Time: 9.65649  | Val Loss: 1.2178 Accuracy: 0.6930\n",
      "Epoch: 9/100 Train Loss: 1.4572 Accuracy: 0.5989 Time: 9.61769  | Val Loss: 1.1732 Accuracy: 0.7129\n",
      "Epoch: 10/100 Train Loss: 1.3961 Accuracy: 0.6176 Time: 9.64574  | Val Loss: 1.2206 Accuracy: 0.6978\n",
      "Epoch: 11/100 Train Loss: 1.3377 Accuracy: 0.6495 Time: 9.65658  | Val Loss: 1.1195 Accuracy: 0.7363\n",
      "Epoch: 12/100 Train Loss: 1.3094 Accuracy: 0.6615 Time: 9.68191  | Val Loss: 1.1163 Accuracy: 0.7401\n",
      "Epoch: 13/100 Train Loss: 1.2707 Accuracy: 0.6760 Time: 9.78913  | Val Loss: 1.0446 Accuracy: 0.7710\n",
      "Epoch: 14/100 Train Loss: 1.2230 Accuracy: 0.6936 Time: 9.73115  | Val Loss: 1.0594 Accuracy: 0.7692\n",
      "Epoch: 15/100 Train Loss: 1.1814 Accuracy: 0.7119 Time: 9.74951  | Val Loss: 0.9930 Accuracy: 0.7992\n",
      "Epoch: 16/100 Train Loss: 1.1600 Accuracy: 0.7239 Time: 9.77501  | Val Loss: 1.0045 Accuracy: 0.7893\n",
      "Epoch: 17/100 Train Loss: 1.1324 Accuracy: 0.7358 Time: 9.75819  | Val Loss: 0.9665 Accuracy: 0.8102\n",
      "Epoch: 18/100 Train Loss: 1.1028 Accuracy: 0.7431 Time: 9.72263  | Val Loss: 0.9454 Accuracy: 0.8209\n",
      "Epoch: 19/100 Train Loss: 1.0802 Accuracy: 0.7595 Time: 9.77360  | Val Loss: 0.9366 Accuracy: 0.8301\n",
      "Epoch: 20/100 Train Loss: 1.0609 Accuracy: 0.7669 Time: 9.81604  | Val Loss: 0.9562 Accuracy: 0.8127\n",
      "Epoch: 21/100 Train Loss: 1.0382 Accuracy: 0.7755 Time: 9.73941  | Val Loss: 0.9280 Accuracy: 0.8303\n",
      "Epoch: 22/100 Train Loss: 1.0274 Accuracy: 0.7789 Time: 9.71772  | Val Loss: 0.9048 Accuracy: 0.8423\n",
      "Epoch: 23/100 Train Loss: 1.0136 Accuracy: 0.7837 Time: 9.78431  | Val Loss: 0.8665 Accuracy: 0.8517\n",
      "Epoch: 24/100 Train Loss: 0.9872 Accuracy: 0.7990 Time: 9.78108  | Val Loss: 0.8948 Accuracy: 0.8413\n",
      "Epoch: 25/100 Train Loss: 0.9637 Accuracy: 0.8060 Time: 9.77327  | Val Loss: 0.8734 Accuracy: 0.8520\n",
      "Epoch: 26/100 Train Loss: 0.9732 Accuracy: 0.7992 Time: 9.76896  | Val Loss: 0.8719 Accuracy: 0.8611\n",
      "Epoch: 27/100 Train Loss: 0.9520 Accuracy: 0.8128 Time: 9.71485  | Val Loss: 0.8373 Accuracy: 0.8650\n",
      "Epoch: 28/100 Train Loss: 0.9360 Accuracy: 0.8206 Time: 9.74664  | Val Loss: 0.8892 Accuracy: 0.8369\n",
      "Epoch: 29/100 Train Loss: 0.9208 Accuracy: 0.8260 Time: 9.75921  | Val Loss: 0.8404 Accuracy: 0.8655\n",
      "Epoch: 30/100 Train Loss: 0.9130 Accuracy: 0.8266 Time: 9.70474  | Val Loss: 0.8439 Accuracy: 0.8637\n",
      "Epoch: 31/100 Train Loss: 0.8919 Accuracy: 0.8413 Time: 9.72200  | Val Loss: 0.8237 Accuracy: 0.8757\n",
      "Epoch: 32/100 Train Loss: 0.8983 Accuracy: 0.8365 Time: 9.72922  | Val Loss: 0.8315 Accuracy: 0.8668\n",
      "Epoch: 33/100 Train Loss: 0.8740 Accuracy: 0.8453 Time: 9.73659  | Val Loss: 0.8269 Accuracy: 0.8729\n",
      "Epoch: 34/100 Train Loss: 0.8565 Accuracy: 0.8567 Time: 9.71826  | Val Loss: 0.8463 Accuracy: 0.8668\n",
      "Epoch: 35/100 Train Loss: 0.8626 Accuracy: 0.8478 Time: 9.75704  | Val Loss: 0.8211 Accuracy: 0.8726\n",
      "Epoch: 36/100 Train Loss: 0.8427 Accuracy: 0.8587 Time: 9.73479  | Val Loss: 0.8170 Accuracy: 0.8787\n",
      "Epoch: 37/100 Train Loss: 0.8387 Accuracy: 0.8600 Time: 9.78536  | Val Loss: 0.8007 Accuracy: 0.8805\n",
      "Epoch: 38/100 Train Loss: 0.8243 Accuracy: 0.8663 Time: 9.73982  | Val Loss: 0.7851 Accuracy: 0.8887\n",
      "Epoch: 39/100 Train Loss: 0.8193 Accuracy: 0.8694 Time: 9.82640  | Val Loss: 0.7779 Accuracy: 0.8866\n",
      "Epoch: 40/100 Train Loss: 0.8246 Accuracy: 0.8661 Time: 9.72839  | Val Loss: 0.7962 Accuracy: 0.8854\n",
      "Epoch: 41/100 Train Loss: 0.8071 Accuracy: 0.8735 Time: 9.73520  | Val Loss: 0.7959 Accuracy: 0.8859\n",
      "Epoch: 42/100 Train Loss: 0.7869 Accuracy: 0.8844 Time: 9.71507  | Val Loss: 0.7809 Accuracy: 0.8859\n",
      "Epoch: 43/100 Train Loss: 0.7813 Accuracy: 0.8862 Time: 9.71013  | Val Loss: 0.7826 Accuracy: 0.8843\n",
      "Epoch: 44/100 Train Loss: 0.7785 Accuracy: 0.8871 Time: 9.75885  | Val Loss: 0.7819 Accuracy: 0.8859\n",
      "Epoch: 45/100 Train Loss: 0.7782 Accuracy: 0.8856 Time: 9.70712  | Val Loss: 0.7714 Accuracy: 0.8897\n",
      "Epoch: 46/100 Train Loss: 0.7634 Accuracy: 0.8905 Time: 9.64437  | Val Loss: 0.7578 Accuracy: 0.8950\n",
      "Epoch: 47/100 Train Loss: 0.7520 Accuracy: 0.8947 Time: 9.75567  | Val Loss: 0.7547 Accuracy: 0.8999\n",
      "Epoch: 48/100 Train Loss: 0.7492 Accuracy: 0.8954 Time: 9.75560  | Val Loss: 0.7620 Accuracy: 0.8920\n",
      "Epoch: 49/100 Train Loss: 0.7396 Accuracy: 0.9042 Time: 9.75241  | Val Loss: 0.7722 Accuracy: 0.8907\n",
      "Epoch: 50/100 Train Loss: 0.7286 Accuracy: 0.9072 Time: 9.79089  | Val Loss: 0.7641 Accuracy: 0.8955\n",
      "Epoch: 51/100 Train Loss: 0.7262 Accuracy: 0.9065 Time: 9.75982  | Val Loss: 0.7615 Accuracy: 0.9039\n",
      "Epoch: 52/100 Train Loss: 0.7160 Accuracy: 0.9140 Time: 9.77236  | Val Loss: 0.7744 Accuracy: 0.8953\n",
      "Epoch: 53/100 Train Loss: 0.7127 Accuracy: 0.9137 Time: 9.74723  | Val Loss: 0.7505 Accuracy: 0.9001\n",
      "Epoch: 54/100 Train Loss: 0.7074 Accuracy: 0.9167 Time: 9.71797  | Val Loss: 0.7390 Accuracy: 0.9068\n",
      "Epoch: 55/100 Train Loss: 0.6899 Accuracy: 0.9214 Time: 9.79131  | Val Loss: 0.7493 Accuracy: 0.8996\n",
      "Epoch: 56/100 Train Loss: 0.6922 Accuracy: 0.9202 Time: 9.83079  | Val Loss: 0.7538 Accuracy: 0.9055\n",
      "Epoch: 57/100 Train Loss: 0.6962 Accuracy: 0.9168 Time: 9.70986  | Val Loss: 0.7483 Accuracy: 0.9047\n",
      "Epoch: 58/100 Train Loss: 0.6759 Accuracy: 0.9286 Time: 9.80712  | Val Loss: 0.7571 Accuracy: 0.8994\n",
      "Epoch: 59/100 Train Loss: 0.6798 Accuracy: 0.9254 Time: 9.72866  | Val Loss: 0.7432 Accuracy: 0.9083\n",
      "Epoch: 60/100 Train Loss: 0.6661 Accuracy: 0.9325 Time: 9.71111  | Val Loss: 0.7429 Accuracy: 0.9070\n",
      "Epoch: 61/100 Train Loss: 0.6605 Accuracy: 0.9359 Time: 9.77368  | Val Loss: 0.7454 Accuracy: 0.9062\n",
      "Epoch: 62/100 Train Loss: 0.6650 Accuracy: 0.9327 Time: 9.77422  | Val Loss: 0.7428 Accuracy: 0.9060\n",
      "Epoch: 63/100 Train Loss: 0.6483 Accuracy: 0.9419 Time: 9.78661  | Val Loss: 0.7453 Accuracy: 0.9075\n",
      "Epoch: 64/100 Train Loss: 0.6560 Accuracy: 0.9376 Time: 9.72197  | Val Loss: 0.7396 Accuracy: 0.9073\n",
      "Epoch: 65/100 Train Loss: 0.6482 Accuracy: 0.9413 Time: 9.79926  | Val Loss: 0.7344 Accuracy: 0.9090\n",
      "Epoch: 66/100 Train Loss: 0.6425 Accuracy: 0.9435 Time: 9.86629  | Val Loss: 0.7441 Accuracy: 0.9075\n",
      "Epoch: 67/100 Train Loss: 0.6309 Accuracy: 0.9472 Time: 9.83580  | Val Loss: 0.7250 Accuracy: 0.9129\n",
      "Epoch: 68/100 Train Loss: 0.6423 Accuracy: 0.9433 Time: 9.75847  | Val Loss: 0.7406 Accuracy: 0.9090\n",
      "Epoch: 69/100 Train Loss: 0.6273 Accuracy: 0.9484 Time: 9.81024  | Val Loss: 0.7390 Accuracy: 0.9065\n",
      "Epoch: 70/100 Train Loss: 0.6239 Accuracy: 0.9528 Time: 9.73888  | Val Loss: 0.7317 Accuracy: 0.9134\n",
      "Epoch: 71/100 Train Loss: 0.6295 Accuracy: 0.9488 Time: 9.75155  | Val Loss: 0.7240 Accuracy: 0.9152\n",
      "Epoch: 72/100 Train Loss: 0.6251 Accuracy: 0.9521 Time: 9.83035  | Val Loss: 0.7237 Accuracy: 0.9164\n",
      "Epoch: 73/100 Train Loss: 0.6139 Accuracy: 0.9555 Time: 9.74904  | Val Loss: 0.7369 Accuracy: 0.9083\n",
      "Epoch: 74/100 Train Loss: 0.6198 Accuracy: 0.9564 Time: 9.72880  | Val Loss: 0.7339 Accuracy: 0.9111\n",
      "Epoch: 75/100 Train Loss: 0.6078 Accuracy: 0.9585 Time: 9.66994  | Val Loss: 0.7314 Accuracy: 0.9139\n",
      "Epoch: 76/100 Train Loss: 0.6093 Accuracy: 0.9577 Time: 9.75522  | Val Loss: 0.7358 Accuracy: 0.9118\n",
      "Epoch: 77/100 Train Loss: 0.6082 Accuracy: 0.9562 Time: 9.71486  | Val Loss: 0.7257 Accuracy: 0.9136\n",
      "Epoch: 78/100 Train Loss: 0.5974 Accuracy: 0.9640 Time: 9.74870  | Val Loss: 0.7265 Accuracy: 0.9152\n",
      "Epoch: 79/100 Train Loss: 0.6041 Accuracy: 0.9598 Time: 9.71235  | Val Loss: 0.7273 Accuracy: 0.9121\n",
      "Epoch: 80/100 Train Loss: 0.6088 Accuracy: 0.9571 Time: 9.76340  | Val Loss: 0.7283 Accuracy: 0.9116\n",
      "Epoch: 81/100 Train Loss: 0.5961 Accuracy: 0.9644 Time: 9.74704  | Val Loss: 0.7256 Accuracy: 0.9126\n",
      "Epoch: 82/100 Train Loss: 0.5940 Accuracy: 0.9641 Time: 9.72579  | Val Loss: 0.7190 Accuracy: 0.9157\n",
      "Epoch: 83/100 Train Loss: 0.5871 Accuracy: 0.9682 Time: 9.72829  | Val Loss: 0.7227 Accuracy: 0.9149\n",
      "Epoch: 84/100 Train Loss: 0.5923 Accuracy: 0.9647 Time: 9.74564  | Val Loss: 0.7235 Accuracy: 0.9162\n",
      "Epoch: 85/100 Train Loss: 0.5885 Accuracy: 0.9665 Time: 9.76186  | Val Loss: 0.7205 Accuracy: 0.9146\n",
      "Epoch: 86/100 Train Loss: 0.5853 Accuracy: 0.9694 Time: 9.75028  | Val Loss: 0.7208 Accuracy: 0.9162\n",
      "Epoch: 87/100 Train Loss: 0.5807 Accuracy: 0.9692 Time: 9.74502  | Val Loss: 0.7239 Accuracy: 0.9149\n",
      "Epoch: 88/100 Train Loss: 0.5867 Accuracy: 0.9669 Time: 9.73599  | Val Loss: 0.7201 Accuracy: 0.9185\n",
      "Epoch: 89/100 Train Loss: 0.5861 Accuracy: 0.9700 Time: 9.78273  | Val Loss: 0.7208 Accuracy: 0.9159\n",
      "Epoch: 90/100 Train Loss: 0.5850 Accuracy: 0.9686 Time: 9.75356  | Val Loss: 0.7251 Accuracy: 0.9146\n",
      "Epoch: 91/100 Train Loss: 0.5779 Accuracy: 0.9714 Time: 9.81525  | Val Loss: 0.7225 Accuracy: 0.9175\n",
      "Epoch: 92/100 Train Loss: 0.5831 Accuracy: 0.9672 Time: 9.76431  | Val Loss: 0.7209 Accuracy: 0.9154\n",
      "Epoch: 93/100 Train Loss: 0.5796 Accuracy: 0.9697 Time: 9.73760  | Val Loss: 0.7223 Accuracy: 0.9146\n",
      "Epoch: 94/100 Train Loss: 0.5785 Accuracy: 0.9703 Time: 9.76409  | Val Loss: 0.7187 Accuracy: 0.9167\n",
      "Epoch: 95/100 Train Loss: 0.5754 Accuracy: 0.9712 Time: 9.76389  | Val Loss: 0.7183 Accuracy: 0.9185\n",
      "Epoch: 96/100 Train Loss: 0.5734 Accuracy: 0.9726 Time: 9.72771  | Val Loss: 0.7186 Accuracy: 0.9154\n",
      "Epoch: 97/100 Train Loss: 0.5756 Accuracy: 0.9707 Time: 9.77041  | Val Loss: 0.7168 Accuracy: 0.9180\n",
      "Epoch: 98/100 Train Loss: 0.5746 Accuracy: 0.9730 Time: 9.75408  | Val Loss: 0.7215 Accuracy: 0.9175\n",
      "Epoch: 99/100 Train Loss: 0.5753 Accuracy: 0.9724 Time: 9.73698  | Val Loss: 0.7228 Accuracy: 0.9134\n",
      "Epoch: 100/100 Train Loss: 0.5765 Accuracy: 0.9702 Time: 9.84645  | Val Loss: 0.7175 Accuracy: 0.9154\n",
      "-----------------------efficientnet_b3------------------------\n",
      "#Parameter: 10711602\n",
      "Epoch: 1/100 Train Loss: 2.7226 Accuracy: 0.1566 Time: 31.48153  | Val Loss: 2.4691 Accuracy: 0.2316\n",
      "Epoch: 2/100 Train Loss: 2.2498 Accuracy: 0.2176 Time: 31.39859  | Val Loss: 2.0551 Accuracy: 0.2775\n",
      "Epoch: 3/100 Train Loss: 2.1352 Accuracy: 0.2551 Time: 31.35265  | Val Loss: 1.8477 Accuracy: 0.4153\n",
      "Epoch: 4/100 Train Loss: 1.9858 Accuracy: 0.3394 Time: 31.14845  | Val Loss: 1.7341 Accuracy: 0.4489\n",
      "Epoch: 5/100 Train Loss: 1.8614 Accuracy: 0.3984 Time: 31.15871  | Val Loss: 1.6077 Accuracy: 0.5062\n",
      "Epoch: 6/100 Train Loss: 1.7366 Accuracy: 0.4655 Time: 31.29457  | Val Loss: 1.5493 Accuracy: 0.5389\n",
      "Epoch: 7/100 Train Loss: 1.6425 Accuracy: 0.5134 Time: 31.29470  | Val Loss: 1.4315 Accuracy: 0.6069\n",
      "Epoch: 8/100 Train Loss: 1.5502 Accuracy: 0.5521 Time: 31.28010  | Val Loss: 1.3470 Accuracy: 0.6476\n",
      "Epoch: 9/100 Train Loss: 1.4572 Accuracy: 0.5973 Time: 31.34788  | Val Loss: 1.2964 Accuracy: 0.6611\n",
      "Epoch: 10/100 Train Loss: 1.3969 Accuracy: 0.6214 Time: 31.34747  | Val Loss: 1.2250 Accuracy: 0.6958\n",
      "Epoch: 11/100 Train Loss: 1.3412 Accuracy: 0.6501 Time: 31.30798  | Val Loss: 1.1186 Accuracy: 0.7427\n",
      "Epoch: 12/100 Train Loss: 1.2965 Accuracy: 0.6682 Time: 31.31259  | Val Loss: 1.1261 Accuracy: 0.7376\n",
      "Epoch: 13/100 Train Loss: 1.2195 Accuracy: 0.6993 Time: 31.25156  | Val Loss: 1.0201 Accuracy: 0.7868\n",
      "Epoch: 14/100 Train Loss: 1.1861 Accuracy: 0.7193 Time: 31.26442  | Val Loss: 1.0492 Accuracy: 0.7758\n",
      "Epoch: 15/100 Train Loss: 1.1619 Accuracy: 0.7236 Time: 31.28481  | Val Loss: 1.0199 Accuracy: 0.7862\n",
      "Epoch: 16/100 Train Loss: 1.1230 Accuracy: 0.7418 Time: 31.13448  | Val Loss: 0.9509 Accuracy: 0.8217\n",
      "Epoch: 17/100 Train Loss: 1.0970 Accuracy: 0.7521 Time: 31.17858  | Val Loss: 0.9534 Accuracy: 0.8204\n",
      "Epoch: 18/100 Train Loss: 1.0706 Accuracy: 0.7647 Time: 31.21266  | Val Loss: 0.9457 Accuracy: 0.8217\n",
      "Epoch: 19/100 Train Loss: 1.0501 Accuracy: 0.7772 Time: 31.33594  | Val Loss: 0.9527 Accuracy: 0.8089\n",
      "Epoch: 20/100 Train Loss: 1.0293 Accuracy: 0.7819 Time: 31.26458  | Val Loss: 0.8898 Accuracy: 0.8380\n",
      "Epoch: 21/100 Train Loss: 0.9947 Accuracy: 0.8002 Time: 31.31093  | Val Loss: 0.8577 Accuracy: 0.8540\n",
      "Epoch: 22/100 Train Loss: 0.9887 Accuracy: 0.8040 Time: 31.27235  | Val Loss: 0.8666 Accuracy: 0.8469\n",
      "Epoch: 23/100 Train Loss: 0.9735 Accuracy: 0.8053 Time: 31.29848  | Val Loss: 0.8558 Accuracy: 0.8535\n",
      "Epoch: 24/100 Train Loss: 0.9325 Accuracy: 0.8219 Time: 31.19138  | Val Loss: 0.8347 Accuracy: 0.8650\n",
      "Epoch: 25/100 Train Loss: 0.9271 Accuracy: 0.8231 Time: 31.35177  | Val Loss: 0.8749 Accuracy: 0.8497\n",
      "Epoch: 26/100 Train Loss: 0.9155 Accuracy: 0.8279 Time: 31.33956  | Val Loss: 0.8011 Accuracy: 0.8797\n",
      "Epoch: 27/100 Train Loss: 0.8964 Accuracy: 0.8376 Time: 31.15329  | Val Loss: 0.8652 Accuracy: 0.8510\n",
      "Epoch: 28/100 Train Loss: 0.8852 Accuracy: 0.8429 Time: 31.15772  | Val Loss: 0.7950 Accuracy: 0.8879\n",
      "Epoch: 29/100 Train Loss: 0.8704 Accuracy: 0.8488 Time: 31.19291  | Val Loss: 0.8039 Accuracy: 0.8810\n",
      "Epoch: 30/100 Train Loss: 0.8622 Accuracy: 0.8528 Time: 31.27514  | Val Loss: 0.7951 Accuracy: 0.8790\n",
      "Epoch: 31/100 Train Loss: 0.8455 Accuracy: 0.8591 Time: 31.26272  | Val Loss: 0.7683 Accuracy: 0.8932\n",
      "Epoch: 32/100 Train Loss: 0.8335 Accuracy: 0.8676 Time: 31.27978  | Val Loss: 0.7886 Accuracy: 0.8856\n",
      "Epoch: 33/100 Train Loss: 0.8261 Accuracy: 0.8695 Time: 31.30337  | Val Loss: 0.7600 Accuracy: 0.8945\n",
      "Epoch: 34/100 Train Loss: 0.8070 Accuracy: 0.8763 Time: 31.33837  | Val Loss: 0.7661 Accuracy: 0.8899\n",
      "Epoch: 35/100 Train Loss: 0.8010 Accuracy: 0.8782 Time: 31.25829  | Val Loss: 0.7667 Accuracy: 0.8948\n",
      "Epoch: 36/100 Train Loss: 0.7842 Accuracy: 0.8838 Time: 31.34587  | Val Loss: 0.7431 Accuracy: 0.9027\n",
      "Epoch: 37/100 Train Loss: 0.7666 Accuracy: 0.8941 Time: 31.29450  | Val Loss: 0.7770 Accuracy: 0.8915\n",
      "Epoch: 38/100 Train Loss: 0.7717 Accuracy: 0.8875 Time: 31.29614  | Val Loss: 0.7490 Accuracy: 0.9001\n",
      "Epoch: 39/100 Train Loss: 0.7636 Accuracy: 0.8931 Time: 31.25715  | Val Loss: 0.7455 Accuracy: 0.8999\n",
      "Epoch: 40/100 Train Loss: 0.7470 Accuracy: 0.9009 Time: 31.24185  | Val Loss: 0.7264 Accuracy: 0.9124\n",
      "Epoch: 41/100 Train Loss: 0.7425 Accuracy: 0.9001 Time: 31.32686  | Val Loss: 0.7487 Accuracy: 0.9022\n",
      "Epoch: 42/100 Train Loss: 0.7323 Accuracy: 0.9045 Time: 31.31791  | Val Loss: 0.7421 Accuracy: 0.8996\n",
      "Epoch: 43/100 Train Loss: 0.7228 Accuracy: 0.9094 Time: 31.28674  | Val Loss: 0.7279 Accuracy: 0.9129\n",
      "Epoch: 44/100 Train Loss: 0.7204 Accuracy: 0.9115 Time: 31.29057  | Val Loss: 0.7392 Accuracy: 0.9121\n",
      "Epoch: 45/100 Train Loss: 0.7064 Accuracy: 0.9171 Time: 31.24676  | Val Loss: 0.7348 Accuracy: 0.9055\n",
      "Epoch: 46/100 Train Loss: 0.7007 Accuracy: 0.9203 Time: 31.26863  | Val Loss: 0.7340 Accuracy: 0.9124\n",
      "Epoch: 47/100 Train Loss: 0.6870 Accuracy: 0.9267 Time: 31.26617  | Val Loss: 0.7343 Accuracy: 0.9103\n",
      "Epoch: 48/100 Train Loss: 0.6830 Accuracy: 0.9264 Time: 31.26254  | Val Loss: 0.7209 Accuracy: 0.9121\n",
      "Epoch: 49/100 Train Loss: 0.6813 Accuracy: 0.9302 Time: 31.27181  | Val Loss: 0.7099 Accuracy: 0.9192\n",
      "Epoch: 50/100 Train Loss: 0.6745 Accuracy: 0.9306 Time: 31.32289  | Val Loss: 0.7150 Accuracy: 0.9167\n",
      "Epoch: 51/100 Train Loss: 0.6627 Accuracy: 0.9380 Time: 31.16732  | Val Loss: 0.7222 Accuracy: 0.9124\n",
      "Epoch: 52/100 Train Loss: 0.6590 Accuracy: 0.9376 Time: 31.20120  | Val Loss: 0.7088 Accuracy: 0.9175\n",
      "Epoch: 53/100 Train Loss: 0.6540 Accuracy: 0.9385 Time: 31.23192  | Val Loss: 0.7198 Accuracy: 0.9139\n",
      "Epoch: 54/100 Train Loss: 0.6406 Accuracy: 0.9461 Time: 31.29276  | Val Loss: 0.7093 Accuracy: 0.9203\n",
      "Epoch: 55/100 Train Loss: 0.6438 Accuracy: 0.9439 Time: 31.28182  | Val Loss: 0.7116 Accuracy: 0.9185\n",
      "Epoch: 56/100 Train Loss: 0.6384 Accuracy: 0.9465 Time: 31.30566  | Val Loss: 0.7126 Accuracy: 0.9185\n",
      "Epoch: 57/100 Train Loss: 0.6362 Accuracy: 0.9456 Time: 31.28000  | Val Loss: 0.7068 Accuracy: 0.9195\n",
      "Epoch: 58/100 Train Loss: 0.6388 Accuracy: 0.9440 Time: 31.33562  | Val Loss: 0.7090 Accuracy: 0.9152\n",
      "Epoch: 59/100 Train Loss: 0.6250 Accuracy: 0.9531 Time: 31.27595  | Val Loss: 0.7001 Accuracy: 0.9200\n",
      "Epoch: 60/100 Train Loss: 0.6159 Accuracy: 0.9563 Time: 31.35817  | Val Loss: 0.7001 Accuracy: 0.9225\n",
      "Epoch: 61/100 Train Loss: 0.6157 Accuracy: 0.9556 Time: 31.33758  | Val Loss: 0.7047 Accuracy: 0.9180\n",
      "Epoch: 62/100 Train Loss: 0.6193 Accuracy: 0.9540 Time: 31.18632  | Val Loss: 0.7009 Accuracy: 0.9251\n",
      "Epoch: 63/100 Train Loss: 0.6009 Accuracy: 0.9637 Time: 31.23492  | Val Loss: 0.6917 Accuracy: 0.9279\n",
      "Epoch: 64/100 Train Loss: 0.5971 Accuracy: 0.9632 Time: 31.19852  | Val Loss: 0.7007 Accuracy: 0.9208\n",
      "Epoch: 65/100 Train Loss: 0.5973 Accuracy: 0.9640 Time: 31.32777  | Val Loss: 0.6835 Accuracy: 0.9297\n",
      "Epoch: 66/100 Train Loss: 0.5897 Accuracy: 0.9667 Time: 31.33122  | Val Loss: 0.6970 Accuracy: 0.9261\n",
      "Epoch: 67/100 Train Loss: 0.5877 Accuracy: 0.9657 Time: 31.24788  | Val Loss: 0.6931 Accuracy: 0.9243\n",
      "Epoch: 68/100 Train Loss: 0.5927 Accuracy: 0.9663 Time: 31.30515  | Val Loss: 0.6949 Accuracy: 0.9231\n",
      "Epoch: 69/100 Train Loss: 0.5844 Accuracy: 0.9693 Time: 31.26652  | Val Loss: 0.6943 Accuracy: 0.9284\n",
      "Epoch: 70/100 Train Loss: 0.5832 Accuracy: 0.9683 Time: 31.29556  | Val Loss: 0.7007 Accuracy: 0.9266\n",
      "Epoch: 71/100 Train Loss: 0.5771 Accuracy: 0.9726 Time: 31.35022  | Val Loss: 0.6881 Accuracy: 0.9307\n",
      "Epoch: 72/100 Train Loss: 0.5719 Accuracy: 0.9737 Time: 31.25135  | Val Loss: 0.6899 Accuracy: 0.9264\n",
      "Epoch: 73/100 Train Loss: 0.5727 Accuracy: 0.9724 Time: 31.29738  | Val Loss: 0.6833 Accuracy: 0.9330\n",
      "Epoch: 74/100 Train Loss: 0.5681 Accuracy: 0.9762 Time: 31.18819  | Val Loss: 0.6891 Accuracy: 0.9276\n",
      "Epoch: 75/100 Train Loss: 0.5717 Accuracy: 0.9740 Time: 31.14129  | Val Loss: 0.6833 Accuracy: 0.9312\n",
      "Epoch: 76/100 Train Loss: 0.5664 Accuracy: 0.9772 Time: 31.30365  | Val Loss: 0.6867 Accuracy: 0.9304\n",
      "Epoch: 77/100 Train Loss: 0.5659 Accuracy: 0.9775 Time: 31.35339  | Val Loss: 0.6961 Accuracy: 0.9256\n",
      "Epoch: 78/100 Train Loss: 0.5638 Accuracy: 0.9768 Time: 31.30938  | Val Loss: 0.6911 Accuracy: 0.9274\n",
      "Epoch: 79/100 Train Loss: 0.5553 Accuracy: 0.9816 Time: 31.31883  | Val Loss: 0.6834 Accuracy: 0.9302\n",
      "Epoch: 80/100 Train Loss: 0.5533 Accuracy: 0.9823 Time: 31.29918  | Val Loss: 0.6830 Accuracy: 0.9299\n",
      "Epoch: 81/100 Train Loss: 0.5568 Accuracy: 0.9806 Time: 31.29460  | Val Loss: 0.6773 Accuracy: 0.9332\n",
      "Epoch: 82/100 Train Loss: 0.5589 Accuracy: 0.9790 Time: 31.29796  | Val Loss: 0.6850 Accuracy: 0.9294\n",
      "Epoch: 83/100 Train Loss: 0.5517 Accuracy: 0.9822 Time: 31.27384  | Val Loss: 0.6822 Accuracy: 0.9322\n",
      "Epoch: 84/100 Train Loss: 0.5536 Accuracy: 0.9815 Time: 31.32765  | Val Loss: 0.6809 Accuracy: 0.9310\n",
      "Epoch: 85/100 Train Loss: 0.5473 Accuracy: 0.9845 Time: 31.27437  | Val Loss: 0.6794 Accuracy: 0.9315\n",
      "Epoch: 86/100 Train Loss: 0.5452 Accuracy: 0.9853 Time: 31.16888  | Val Loss: 0.6822 Accuracy: 0.9312\n",
      "Epoch: 87/100 Train Loss: 0.5482 Accuracy: 0.9839 Time: 31.22084  | Val Loss: 0.6804 Accuracy: 0.9322\n",
      "Epoch: 88/100 Train Loss: 0.5503 Accuracy: 0.9820 Time: 31.31936  | Val Loss: 0.6762 Accuracy: 0.9338\n",
      "Epoch: 89/100 Train Loss: 0.5442 Accuracy: 0.9860 Time: 31.30760  | Val Loss: 0.6796 Accuracy: 0.9322\n",
      "Epoch: 90/100 Train Loss: 0.5436 Accuracy: 0.9862 Time: 31.36310  | Val Loss: 0.6784 Accuracy: 0.9302\n",
      "Epoch: 91/100 Train Loss: 0.5417 Accuracy: 0.9866 Time: 31.30897  | Val Loss: 0.6785 Accuracy: 0.9332\n",
      "Epoch: 92/100 Train Loss: 0.5443 Accuracy: 0.9842 Time: 31.24821  | Val Loss: 0.6734 Accuracy: 0.9361\n",
      "Epoch: 93/100 Train Loss: 0.5398 Accuracy: 0.9864 Time: 31.30789  | Val Loss: 0.6758 Accuracy: 0.9353\n",
      "Epoch: 94/100 Train Loss: 0.5401 Accuracy: 0.9866 Time: 31.32109  | Val Loss: 0.6759 Accuracy: 0.9340\n",
      "Epoch: 95/100 Train Loss: 0.5464 Accuracy: 0.9846 Time: 31.33930  | Val Loss: 0.6750 Accuracy: 0.9345\n",
      "Epoch: 96/100 Train Loss: 0.5457 Accuracy: 0.9841 Time: 31.28801  | Val Loss: 0.6759 Accuracy: 0.9343\n",
      "Epoch: 97/100 Train Loss: 0.5387 Accuracy: 0.9868 Time: 31.19395  | Val Loss: 0.6766 Accuracy: 0.9345\n",
      "Epoch: 98/100 Train Loss: 0.5405 Accuracy: 0.9867 Time: 31.20326  | Val Loss: 0.6741 Accuracy: 0.9353\n",
      "Epoch: 99/100 Train Loss: 0.5374 Accuracy: 0.9881 Time: 31.30938  | Val Loss: 0.6775 Accuracy: 0.9315\n",
      "Epoch: 100/100 Train Loss: 0.5383 Accuracy: 0.9883 Time: 31.28593  | Val Loss: 0.6732 Accuracy: 0.9353\n",
      "-----------------------efficientnet_b5------------------------\n",
      "#Parameter: 28361274\n",
      "Epoch: 1/100 Train Loss: 2.5150 Accuracy: 0.1147 Time: 121.81808  | Val Loss: 2.2917 Accuracy: 0.1488\n",
      "Epoch: 2/100 Train Loss: 2.2909 Accuracy: 0.1477 Time: 121.77960  | Val Loss: 2.1769 Accuracy: 0.2102\n",
      "Epoch: 3/100 Train Loss: 2.2403 Accuracy: 0.1783 Time: 121.51314  | Val Loss: 2.2759 Accuracy: 0.2155\n",
      "Epoch: 4/100 Train Loss: 2.1112 Accuracy: 0.2535 Time: 121.92034  | Val Loss: 1.9086 Accuracy: 0.3383\n",
      "Epoch: 5/100 Train Loss: 2.0146 Accuracy: 0.3135 Time: 121.85817  | Val Loss: 1.8125 Accuracy: 0.4161\n",
      "Epoch: 6/100 Train Loss: 1.9267 Accuracy: 0.3591 Time: 121.58403  | Val Loss: 1.7492 Accuracy: 0.4454\n",
      "Epoch: 7/100 Train Loss: 1.8572 Accuracy: 0.3947 Time: 121.84240  | Val Loss: 1.6330 Accuracy: 0.5075\n",
      "Epoch: 8/100 Train Loss: 1.7862 Accuracy: 0.4410 Time: 121.92428  | Val Loss: 1.6159 Accuracy: 0.5292\n",
      "Epoch: 9/100 Train Loss: 1.7231 Accuracy: 0.4689 Time: 121.50181  | Val Loss: 1.4287 Accuracy: 0.6239\n",
      "Epoch: 10/100 Train Loss: 1.6350 Accuracy: 0.5124 Time: 121.71503  | Val Loss: 1.3828 Accuracy: 0.6311\n",
      "Epoch: 11/100 Train Loss: 1.5752 Accuracy: 0.5363 Time: 121.85409  | Val Loss: 1.3116 Accuracy: 0.6660\n",
      "Epoch: 12/100 Train Loss: 1.5032 Accuracy: 0.5781 Time: 121.40180  | Val Loss: 1.2325 Accuracy: 0.6994\n",
      "Epoch: 13/100 Train Loss: 1.4649 Accuracy: 0.5991 Time: 121.88804  | Val Loss: 1.2021 Accuracy: 0.7131\n",
      "Epoch: 14/100 Train Loss: 1.4127 Accuracy: 0.6220 Time: 121.82070  | Val Loss: 1.1532 Accuracy: 0.7404\n",
      "Epoch: 15/100 Train Loss: 1.3754 Accuracy: 0.6401 Time: 121.51046  | Val Loss: 1.1320 Accuracy: 0.7518\n",
      "Epoch: 16/100 Train Loss: 1.3130 Accuracy: 0.6605 Time: 121.96478  | Val Loss: 1.0905 Accuracy: 0.7717\n",
      "Epoch: 17/100 Train Loss: 1.2714 Accuracy: 0.6805 Time: 121.88211  | Val Loss: 1.0273 Accuracy: 0.8020\n",
      "Epoch: 18/100 Train Loss: 1.2260 Accuracy: 0.7086 Time: 121.54553  | Val Loss: 1.0157 Accuracy: 0.8074\n",
      "Epoch: 19/100 Train Loss: 1.1986 Accuracy: 0.7163 Time: 121.81758  | Val Loss: 0.9933 Accuracy: 0.8163\n",
      "Epoch: 20/100 Train Loss: 1.1763 Accuracy: 0.7307 Time: 121.82616  | Val Loss: 0.9715 Accuracy: 0.8308\n",
      "Epoch: 21/100 Train Loss: 1.1461 Accuracy: 0.7412 Time: 121.54693  | Val Loss: 0.9565 Accuracy: 0.8334\n",
      "Epoch: 22/100 Train Loss: 1.1000 Accuracy: 0.7628 Time: 121.85782  | Val Loss: 0.9669 Accuracy: 0.8265\n",
      "Epoch: 23/100 Train Loss: 1.0938 Accuracy: 0.7623 Time: 121.91696  | Val Loss: 0.9148 Accuracy: 0.8535\n",
      "Epoch: 24/100 Train Loss: 1.0658 Accuracy: 0.7732 Time: 121.74055  | Val Loss: 0.9138 Accuracy: 0.8507\n",
      "Epoch: 25/100 Train Loss: 1.0505 Accuracy: 0.7883 Time: 121.81407  | Val Loss: 0.8803 Accuracy: 0.8566\n",
      "Epoch: 26/100 Train Loss: 1.0243 Accuracy: 0.7948 Time: 121.75765  | Val Loss: 0.8484 Accuracy: 0.8726\n",
      "Epoch: 27/100 Train Loss: 1.0073 Accuracy: 0.7972 Time: 121.67619  | Val Loss: 0.8503 Accuracy: 0.8703\n",
      "Epoch: 28/100 Train Loss: 0.9916 Accuracy: 0.8125 Time: 121.72199  | Val Loss: 0.8525 Accuracy: 0.8690\n",
      "Epoch: 29/100 Train Loss: 0.9892 Accuracy: 0.8076 Time: 121.78652  | Val Loss: 0.8581 Accuracy: 0.8746\n",
      "Epoch: 30/100 Train Loss: 0.9575 Accuracy: 0.8189 Time: 121.77846  | Val Loss: 0.8404 Accuracy: 0.8785\n",
      "Epoch: 31/100 Train Loss: 0.9406 Accuracy: 0.8292 Time: 121.80805  | Val Loss: 0.8497 Accuracy: 0.8757\n",
      "Epoch: 32/100 Train Loss: 0.9360 Accuracy: 0.8270 Time: 121.78008  | Val Loss: 0.8135 Accuracy: 0.8915\n",
      "Epoch: 33/100 Train Loss: 0.9190 Accuracy: 0.8382 Time: 121.65208  | Val Loss: 0.8265 Accuracy: 0.8838\n",
      "Epoch: 34/100 Train Loss: 0.8948 Accuracy: 0.8500 Time: 121.71587  | Val Loss: 0.8149 Accuracy: 0.8894\n",
      "Epoch: 35/100 Train Loss: 0.8801 Accuracy: 0.8524 Time: 121.83515  | Val Loss: 0.7943 Accuracy: 0.8966\n",
      "Epoch: 36/100 Train Loss: 0.8718 Accuracy: 0.8563 Time: 121.85385  | Val Loss: 0.7772 Accuracy: 0.9004\n",
      "Epoch: 37/100 Train Loss: 0.8589 Accuracy: 0.8620 Time: 121.66862  | Val Loss: 0.7900 Accuracy: 0.8953\n",
      "Epoch: 38/100 Train Loss: 0.8567 Accuracy: 0.8631 Time: 121.83409  | Val Loss: 0.7776 Accuracy: 0.8971\n",
      "Epoch: 39/100 Train Loss: 0.8362 Accuracy: 0.8727 Time: 121.78117  | Val Loss: 0.7763 Accuracy: 0.8989\n",
      "Epoch: 40/100 Train Loss: 0.8201 Accuracy: 0.8799 Time: 121.81248  | Val Loss: 0.7621 Accuracy: 0.9045\n",
      "Epoch: 41/100 Train Loss: 0.8148 Accuracy: 0.8762 Time: 121.76602  | Val Loss: 0.7589 Accuracy: 0.9011\n",
      "Epoch: 42/100 Train Loss: 0.8011 Accuracy: 0.8835 Time: 121.94630  | Val Loss: 0.7620 Accuracy: 0.9006\n",
      "Epoch: 43/100 Train Loss: 0.7943 Accuracy: 0.8883 Time: 121.68723  | Val Loss: 0.7519 Accuracy: 0.9083\n",
      "Epoch: 44/100 Train Loss: 0.7890 Accuracy: 0.8899 Time: 121.87454  | Val Loss: 0.7580 Accuracy: 0.9034\n",
      "Epoch: 45/100 Train Loss: 0.7828 Accuracy: 0.8886 Time: 121.84124  | Val Loss: 0.7713 Accuracy: 0.8999\n",
      "Epoch: 46/100 Train Loss: 0.7700 Accuracy: 0.8977 Time: 121.52832  | Val Loss: 0.7781 Accuracy: 0.8973\n",
      "Epoch: 47/100 Train Loss: 0.7548 Accuracy: 0.9034 Time: 122.00312  | Val Loss: 0.7602 Accuracy: 0.9034\n",
      "Epoch: 48/100 Train Loss: 0.7508 Accuracy: 0.9070 Time: 121.87681  | Val Loss: 0.7619 Accuracy: 0.9004\n",
      "Epoch: 49/100 Train Loss: 0.7344 Accuracy: 0.9127 Time: 121.55344  | Val Loss: 0.7439 Accuracy: 0.9098\n",
      "Epoch: 50/100 Train Loss: 0.7259 Accuracy: 0.9148 Time: 121.84687  | Val Loss: 0.7469 Accuracy: 0.9070\n",
      "Epoch: 51/100 Train Loss: 0.7266 Accuracy: 0.9136 Time: 121.74141  | Val Loss: 0.7413 Accuracy: 0.9121\n",
      "Epoch: 52/100 Train Loss: 0.7191 Accuracy: 0.9185 Time: 121.50538  | Val Loss: 0.7254 Accuracy: 0.9205\n",
      "Epoch: 53/100 Train Loss: 0.7124 Accuracy: 0.9204 Time: 121.85063  | Val Loss: 0.7236 Accuracy: 0.9185\n",
      "Epoch: 54/100 Train Loss: 0.7026 Accuracy: 0.9230 Time: 121.83594  | Val Loss: 0.7324 Accuracy: 0.9152\n",
      "Epoch: 55/100 Train Loss: 0.7012 Accuracy: 0.9248 Time: 121.57787  | Val Loss: 0.7261 Accuracy: 0.9185\n",
      "Epoch: 56/100 Train Loss: 0.6866 Accuracy: 0.9303 Time: 121.87341  | Val Loss: 0.7223 Accuracy: 0.9152\n",
      "Epoch: 57/100 Train Loss: 0.6815 Accuracy: 0.9342 Time: 121.81449  | Val Loss: 0.7309 Accuracy: 0.9172\n",
      "Epoch: 58/100 Train Loss: 0.6759 Accuracy: 0.9351 Time: 121.64984  | Val Loss: 0.7298 Accuracy: 0.9139\n",
      "Epoch: 59/100 Train Loss: 0.6730 Accuracy: 0.9354 Time: 121.80187  | Val Loss: 0.7179 Accuracy: 0.9187\n",
      "Epoch: 60/100 Train Loss: 0.6703 Accuracy: 0.9378 Time: 121.88336  | Val Loss: 0.7302 Accuracy: 0.9126\n",
      "Epoch: 61/100 Train Loss: 0.6660 Accuracy: 0.9385 Time: 121.55653  | Val Loss: 0.7137 Accuracy: 0.9192\n",
      "Epoch: 62/100 Train Loss: 0.6533 Accuracy: 0.9438 Time: 121.79249  | Val Loss: 0.7204 Accuracy: 0.9180\n",
      "Epoch: 63/100 Train Loss: 0.6478 Accuracy: 0.9456 Time: 121.77897  | Val Loss: 0.7084 Accuracy: 0.9238\n",
      "Epoch: 64/100 Train Loss: 0.6523 Accuracy: 0.9434 Time: 121.44574  | Val Loss: 0.7086 Accuracy: 0.9215\n",
      "Epoch: 65/100 Train Loss: 0.6419 Accuracy: 0.9488 Time: 121.98944  | Val Loss: 0.7123 Accuracy: 0.9236\n",
      "Epoch: 66/100 Train Loss: 0.6388 Accuracy: 0.9470 Time: 121.89904  | Val Loss: 0.7071 Accuracy: 0.9254\n",
      "Epoch: 67/100 Train Loss: 0.6300 Accuracy: 0.9532 Time: 121.58533  | Val Loss: 0.7060 Accuracy: 0.9246\n",
      "Epoch: 68/100 Train Loss: 0.6245 Accuracy: 0.9552 Time: 121.82230  | Val Loss: 0.6960 Accuracy: 0.9269\n",
      "Epoch: 69/100 Train Loss: 0.6213 Accuracy: 0.9558 Time: 122.02329  | Val Loss: 0.6942 Accuracy: 0.9261\n",
      "Epoch: 70/100 Train Loss: 0.6124 Accuracy: 0.9599 Time: 121.61007  | Val Loss: 0.7035 Accuracy: 0.9225\n",
      "Epoch: 71/100 Train Loss: 0.6144 Accuracy: 0.9606 Time: 121.86088  | Val Loss: 0.6934 Accuracy: 0.9261\n",
      "Epoch: 72/100 Train Loss: 0.6126 Accuracy: 0.9606 Time: 121.90525  | Val Loss: 0.6922 Accuracy: 0.9274\n",
      "Epoch: 73/100 Train Loss: 0.6073 Accuracy: 0.9617 Time: 121.57249  | Val Loss: 0.7056 Accuracy: 0.9213\n",
      "Epoch: 74/100 Train Loss: 0.6009 Accuracy: 0.9659 Time: 121.94061  | Val Loss: 0.6981 Accuracy: 0.9276\n",
      "Epoch: 75/100 Train Loss: 0.6024 Accuracy: 0.9649 Time: 121.73035  | Val Loss: 0.6935 Accuracy: 0.9269\n",
      "Epoch: 76/100 Train Loss: 0.6019 Accuracy: 0.9646 Time: 121.73723  | Val Loss: 0.6968 Accuracy: 0.9264\n",
      "Epoch: 77/100 Train Loss: 0.5955 Accuracy: 0.9675 Time: 121.83402  | Val Loss: 0.6844 Accuracy: 0.9307\n",
      "Epoch: 78/100 Train Loss: 0.5896 Accuracy: 0.9710 Time: 121.82016  | Val Loss: 0.6873 Accuracy: 0.9294\n",
      "Epoch: 79/100 Train Loss: 0.5960 Accuracy: 0.9655 Time: 121.75678  | Val Loss: 0.6912 Accuracy: 0.9282\n",
      "Epoch: 80/100 Train Loss: 0.5879 Accuracy: 0.9699 Time: 121.79325  | Val Loss: 0.6834 Accuracy: 0.9299\n",
      "Epoch: 81/100 Train Loss: 0.5858 Accuracy: 0.9690 Time: 121.91672  | Val Loss: 0.6911 Accuracy: 0.9287\n",
      "Epoch: 82/100 Train Loss: 0.5835 Accuracy: 0.9723 Time: 121.93053  | Val Loss: 0.6790 Accuracy: 0.9317\n",
      "Epoch: 83/100 Train Loss: 0.5774 Accuracy: 0.9751 Time: 121.84266  | Val Loss: 0.6819 Accuracy: 0.9317\n",
      "Epoch: 84/100 Train Loss: 0.5773 Accuracy: 0.9744 Time: 121.87120  | Val Loss: 0.6788 Accuracy: 0.9315\n",
      "Epoch: 85/100 Train Loss: 0.5754 Accuracy: 0.9745 Time: 121.75690  | Val Loss: 0.6759 Accuracy: 0.9340\n",
      "Epoch: 86/100 Train Loss: 0.5806 Accuracy: 0.9714 Time: 121.72332  | Val Loss: 0.6778 Accuracy: 0.9315\n",
      "Epoch: 87/100 Train Loss: 0.5742 Accuracy: 0.9760 Time: 121.92511  | Val Loss: 0.6810 Accuracy: 0.9299\n",
      "Epoch: 88/100 Train Loss: 0.5733 Accuracy: 0.9739 Time: 121.71881  | Val Loss: 0.6744 Accuracy: 0.9330\n",
      "Epoch: 89/100 Train Loss: 0.5761 Accuracy: 0.9744 Time: 121.62093  | Val Loss: 0.6767 Accuracy: 0.9348\n",
      "Epoch: 90/100 Train Loss: 0.5691 Accuracy: 0.9779 Time: 121.85887  | Val Loss: 0.6816 Accuracy: 0.9315\n",
      "Epoch: 91/100 Train Loss: 0.5692 Accuracy: 0.9767 Time: 121.85692  | Val Loss: 0.6755 Accuracy: 0.9317\n",
      "Epoch: 92/100 Train Loss: 0.5677 Accuracy: 0.9769 Time: 121.69290  | Val Loss: 0.6741 Accuracy: 0.9343\n",
      "Epoch: 93/100 Train Loss: 0.5676 Accuracy: 0.9773 Time: 121.89394  | Val Loss: 0.6818 Accuracy: 0.9304\n",
      "Epoch: 94/100 Train Loss: 0.5700 Accuracy: 0.9754 Time: 121.81620  | Val Loss: 0.6759 Accuracy: 0.9327\n",
      "Epoch: 95/100 Train Loss: 0.5628 Accuracy: 0.9813 Time: 121.65925  | Val Loss: 0.6781 Accuracy: 0.9325\n",
      "Epoch: 96/100 Train Loss: 0.5647 Accuracy: 0.9775 Time: 121.92498  | Val Loss: 0.6721 Accuracy: 0.9327\n",
      "Epoch: 97/100 Train Loss: 0.5663 Accuracy: 0.9791 Time: 121.93274  | Val Loss: 0.6750 Accuracy: 0.9338\n",
      "Epoch: 98/100 Train Loss: 0.5666 Accuracy: 0.9776 Time: 121.82088  | Val Loss: 0.6713 Accuracy: 0.9353\n",
      "Epoch: 99/100 Train Loss: 0.5619 Accuracy: 0.9815 Time: 121.82623  | Val Loss: 0.6715 Accuracy: 0.9340\n",
      "Epoch: 100/100 Train Loss: 0.5671 Accuracy: 0.9758 Time: 121.95672  | Val Loss: 0.6694 Accuracy: 0.9350\n"
     ]
    }
   ],
   "source": [
    "from venv import create\n",
    "from hdd.data_util.auto_augmentation import ImageNetPolicy\n",
    "\n",
    "from hdd.data_util.transforms import RandomResize\n",
    "from torch.utils.data import DataLoader\n",
    "from hdd.models.cnn.efficientnet import create_efficient_net\n",
    "from hdd.train.classification_utils import (\n",
    "    naive_train_classification_model,\n",
    ")\n",
    "from hdd.models.nn_utils import count_trainable_parameter\n",
    "\n",
    "\n",
    "def train_net(\n",
    "    net,\n",
    "    train_dataloader,\n",
    "    val_dataloader,\n",
    "    lr=1e-3,\n",
    "    weight_decay=1e-3,\n",
    "    max_epochs=100,\n",
    ") -> dict[str, list[float]]:\n",
    "    print(f\"#Parameter: {count_trainable_parameter(net)}\")\n",
    "    criteria = nn.CrossEntropyLoss(label_smoothing=0.1)\n",
    "    optimizer = torch.optim.AdamW(net.parameters(), lr=lr, weight_decay=weight_decay)\n",
    "    scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(\n",
    "        optimizer, max_epochs, eta_min=lr / 100\n",
    "    )\n",
    "    training_stats = naive_train_classification_model(\n",
    "        net,\n",
    "        criteria,\n",
    "        max_epochs,\n",
    "        train_dataloader,\n",
    "        val_dataloader,\n",
    "        DEVICE,\n",
    "        optimizer,\n",
    "        scheduler,\n",
    "        verbose=True,\n",
    "    )\n",
    "    return training_stats\n",
    "\n",
    "\n",
    "def build_dataloader(batch_size, train_dataset, val_dataset):\n",
    "    train_dataloader = DataLoader(\n",
    "        train_dataset, batch_size=batch_size, shuffle=True, num_workers=8\n",
    "    )\n",
    "    val_dataloader = DataLoader(\n",
    "        val_dataset, batch_size=batch_size, shuffle=False, num_workers=8\n",
    "    )\n",
    "    return train_dataloader, val_dataloader\n",
    "\n",
    "\n",
    "# 我们提前计算好了训练数据集上的均值和方差\n",
    "TRAIN_MEAN = [0.4625, 0.4580, 0.4295]\n",
    "TRAIN_STD = [0.2452, 0.2390, 0.2469]\n",
    "\n",
    "stats = {}\n",
    "net_names = [\"efficientnet_b0\", \"efficientnet_b3\", \"efficientnet_b5\"]\n",
    "batch_sizes = [64, 32, 8]\n",
    "for net_name, batch_size in zip(net_names, batch_sizes):\n",
    "    net, resize_size, crop_size = create_efficient_net(net_name, 0.1, 10)\n",
    "    net = net.to(DEVICE)\n",
    "    train_dataset_transforms = transforms.Compose(\n",
    "        [\n",
    "            transforms.Resize(resize_size),\n",
    "            transforms.RandomCrop(crop_size),\n",
    "            transforms.RandomHorizontalFlip(),\n",
    "            ImageNetPolicy(),\n",
    "            transforms.ToTensor(),\n",
    "            transforms.Normalize(mean=TRAIN_MEAN, std=TRAIN_STD),\n",
    "        ]\n",
    "    )\n",
    "    train_dataset.transform = train_dataset_transforms\n",
    "    val_dataset_transforms = transforms.Compose(\n",
    "        [\n",
    "            transforms.Resize(resize_size),\n",
    "            transforms.CenterCrop(crop_size),\n",
    "            transforms.ToTensor(),\n",
    "            transforms.Normalize(mean=TRAIN_MEAN, std=TRAIN_STD),\n",
    "        ]\n",
    "    )\n",
    "    val_dataset.transform = val_dataset_transforms\n",
    "    train_dataloader, val_dataloader = build_dataloader(\n",
    "        batch_size, train_dataset, val_dataset\n",
    "    )\n",
    "    print(f\"-----------------------{net_name}------------------------\")\n",
    "    max_epochs = 100\n",
    "    stats[net_name] = train_net(\n",
    "        net,\n",
    "        train_dataloader,\n",
    "        val_dataloader,\n",
    "        lr=0.001,\n",
    "        weight_decay=0,\n",
    "        max_epochs=max_epochs,\n",
    "    )\n",
    "    del net"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "f83778b3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x1200 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "plt.figure(figsize=(12,12))\n",
    "fields = stats[net_names[0]].keys()\n",
    "for i, field in enumerate(fields):\n",
    "    plt.subplot(2, 2, i+1)\n",
    "    plt.plot(stats[net_names[0]][field], label=net_names[0], linestyle=\"--\")\n",
    "    plt.plot(stats[net_names[1]][field], label=net_names[1])\n",
    "    plt.plot(stats[net_names[2]][field], label=net_names[2], linestyle=\"--\")\n",
    "    plt.legend()\n",
    "    plt.title(field)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7096508b",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "pytorch-cu124",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.11"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
